Server Quality Checklist

Tool Scores

• cross_reference_checkB
  3.2/5.0
  Behavior2/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries full behavioral disclosure burden. It mentions outputs (consistency score and detailed comparison) which is helpful, but doesn't disclose any side effects, failure modes, data handling, or what happens on mismatch. For a data-comparison tool, details on normalization rules or error behavior would be valuable.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Three concise sentences with no wasted words, and the output type is front-loaded. Slightly deeper behavioral context could be added, but the current length is efficient and appropriate.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  With no output schema and no annotations, the description should disclose more about return shape and side effects. It mentions 'consistency score and detailed comparison' which helps, but doesn't cover edge cases like empty reference arrays, handling of missing comparison fields, or the meaning of tolerance levels. Adequate but with notable gaps given the zero annotation coverage.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so all 4 parameters are documented in the schema. The description adds nothing about parameter semantics beyond the schema. Baseline 3 applies since the schema does the heavy lifting and the description doesn't need to compensate.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool compares data from multiple agents and detects discrepancies, with a specific verb (compare) and resource (data from agents). It returns a consistency score. However, it doesn't explicitly differentiate from siblings like detect_hallucination_markers or output_consistency_score, which could overlap in purpose, so it doesn't fully earn a 5.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  'Essential for multi-agent coordination' provides context on when to use it. However, there are no explicit exclusions or when-not-to-use guidance, and sibling tools like output_consistency_score likely overlap significantly in purpose, so the description fails to help the agent choose between them.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• output_consistency_scoreB
  3.4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It says the tool 'returns comprehensive reliability assessment' and covers 'internal logic, format consistency, and reliability indicators' — which gives some insight into what the output contains. However, it doesn't disclose what input is required beyond the schema, whether it's a read-only analysis operation, what happens with missing context fields, or how the score is calibrated/normalized. For a scoring tool with no annotations, more behavioral context would help.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is a single, compact sentence that efficiently conveys the tool's purpose and its key scoring dimensions. It's front-loaded with the core action ('calculate overall consistency score') and adds relevant detail about what dimensions are covered. No wasted words or redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  This is a moderately complex scoring tool with 5 parameters, one nested object (historical_outputs), and no output schema to clarify the return value. The description explains what's being scored but doesn't explain the score format, scale, or how to interpret the 'comprehensive reliability assessment' output. Given the complexity (nested objects, multiple optional inputs) and lack of an output schema, the description could benefit from explaining how the various parameters factor into the score.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, meaning all 5 parameters have descriptions in the schema itself. The description adds the meaning that 'output' is the primary input and frames the tool's purpose around scoring it. However, the description doesn't add parameter-level semantics beyond the schema — it doesn't clarify what 'context' adds to scoring, how historical_outputs weighting works, or what expected_format contributes. Baseline 3 is appropriate since the schema is fully descriptive and the description doesn't materially deepen parameter understanding.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description uses a specific verb ('calculate') with a clear resource ('overall consistency score for agent output') and lists the score dimensions (internal logic, format consistency, reliability). It's clear about what the tool does. However, it doesn't explicitly distinguish from siblings like detect_hallucination_markers or cross_reference_check, though the mention of 'overall consistency' hints at the holistic purpose versus the more targeted sibling tools.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description implies this is for holistic output assessment (calculating an overall score), which is somewhat distinct from the sibling tools (verify_json_schema is structural, detect_hallucination_markers is specific, cross_reference_check is relational). However, there's no explicit 'when to use this vs alternatives' guidance or exclusion criteria. The usage context is only implied through the 'overall' framing.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• validate_data_freshnessB
  3.4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the full burden. It discloses it checks timestamps, staleness, and update frequency, and states the outcome purpose (prevent acting on outdated info), but doesn't describe return format, what constitutes 'valid', or whether it fails hard or soft. Moderate disclosure for a read-style check tool.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Two concise sentences with no wasted words. The second sentence effectively frames the purpose. It's appropriately sized for the tool's complexity, though could arguably add a note about return behavior without much cost.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  With 5 parameters, 100% schema coverage, no annotations, and no output schema, the description handles the purpose but leaves ambiguous what the return value looks like and how staleness/update-frequency signals interact with max_age_hours. For a validation tool, the outcome semantics are important for an agent to interpret the result.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so each of the 5 parameters is already documented in the schema. The description adds a general framing and the 'Prevents acting on outdated information' rationale but does not add syntax, defaults rationale, or interaction details beyond the schema. Baseline 3 is appropriate.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description states a specific verb+resource ('Check if data...is recent and valid') and mentions the key signals (timestamps, staleness indicators, update frequencies). It's clear and distinct from siblings like validate_json_schema or cross_reference_check, which handle different validation concerns.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description implies the tool is used before acting on data ('Prevents acting on outdated information') which gives context, but it doesn't explicitly compare against alternatives or state when NOT to use it. No exclusions or alternative tool references are present.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• verify_json_schemaB
  3.4/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  With no annotations provided, the description carries the burden of behavioral disclosure. It does state the return content ('validation status, errors, and confidence score') which is genuinely useful. However, it doesn't disclose behavioral details like failure modes, how strict_validation interacts with behavior, performance constraints, or what happens with invalid inputs. For a validation tool, the return value disclosure is the key behavioral trait and it's covered, but other aspects are thin.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is three sentences: purpose, importance/context, and return value summary. It's appropriately compact with no fluff. The front-loading is good—the first sentence states the core purpose immediately. The middle sentence justifies importance (essential for preventing malformed data propagation) which is arguably the weakest element since it's slightly promotional, but it's short and doesn't waste space.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  The tool has 4 parameters, nested objects, no output schema, and no annotations. This is moderate complexity. The description covers the purpose, use case context, and return values, which covers the main gaps left by the lack of output schema. However, given no annotations exist, the description could add more behavioral detail (e.g., how strict_validation changes outcomes, failure handling). For a validation tool, the non-obvious behavior is borderline adequately covered.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 100%, so all 4 parameters are documented in the schema itself. Per the rubric, this sets a baseline of 3. The description adds limited param context—it mentions 'strict validation mode' indirectly through context but doesn't elaborate beyond the schema. The description's statement about returning 'errors' and 'confidence score' partially explains what data validation produces but doesn't add param-level depth.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the verb (Validate), resource (JSON data), and object (against expected schema). It distinguishes itself from sibling tools by focusing on schema validation specifically, whereas siblings handle hallucination detection, data freshness, cross-referencing, and output consistency. However, it doesn't explicitly compare itself to siblings, and the name itself already conveys most of the purpose.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines3/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description mentions 'in multi-agent workflows' and 'data from another agent', which gives clear context for when to use it. However, it doesn't explicitly state when NOT to use it or mention alternatives among the sibling tools (e.g., when to use validate_data_freshness vs. verify_json_schema). The guidance is adequate but implied rather than explicit.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

• detect_hallucination_markersA
  3.9/5.0
  Behavior3/5

  Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

  No annotations are provided, so the description carries the safety burden. It discloses that it returns 'detailed analysis and confidence score', and mentions the tool is a scan (read-only, non-destructive). However, it doesn't disclose whether detection is deterministic or heuristic, whether sensitivity_level thresholds affect the confidence score, or how content_type context changes behavior. Moderate transparency for a read-oriented analysis tool with no annotations.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Three sentences, front-loaded with the primary action, plus a purpose-driven rationale ('Critical for multi-agent reliability') and a return-value note. Each sentence earns its place; no filler or redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a parameter-rich analysis tool with no annotations and no output schema, the description reasonably covers purpose, return value, and reliability context. It could add more about output structure given there's no output schema (what does 'detailed analysis' contain?), but the 100% schema coverage compensates for parameter understanding. Slight gap on behavioral details but adequate overall.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, so the schema already documents all 4 parameters with descriptions. The description adds marginal value by naming key behaviors (patterns, uncertainty markers, fabrication indicators) that relate to content_type context. However, the description doesn't elaborate on how sensitivity_level or source_agent affect output beyond what the schema enum values imply. Baseline 3 is appropriate given full schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description has a specific verb ('scan') and resource ('agent output'), clearly states what it detects (hallucination patterns, uncertainty markers, fabrication indicators), and distinguishes it from siblings like verify_json_schema and cross_reference_check (these focus on structured validation, not semantic content analysis).

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description states it's 'critical for multi-agent reliability', implying use when validating outputs from other agents in a pipeline. However, it doesn't explicitly explain when NOT to use it versus alternatives like cross_reference_check or output_consistency_score, leaving the decision among quality tools somewhat implicit.

  Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.